A Comparative Analysis of Machine Learning Classifier of Anemia Diagnosis Based on Complete Blood Count (CBC) Data

Abstract

Anemia is a prevalent hematological condition that requires accurate and timely diagnosis to ensure effective treatment. This study aims to compare the performance of several machine learning algorithms Random Forest, Support Vector Machine (SVM), Naive Bayes, and XGBoost in classifying different types of anemia based on Complete Blood Count (CBC) data. The dataset includes three diagnostic categories: Healthy, Normocytic hypochromic anemia, and Normocytic normochromic anemia. After preprocessing and normalization, each model was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that XGBoost achieved the highest overall performance with 99% accuracy and a perfect AUC of 1.00, followed closely by SVM and Naive Bayes. Naive Bayes showed lower performance, particularly in identifying normocytic normochromic anemia. These findings suggest that machine learning, especially ensemble-based models, holds strong potential in supporting clinical diagnosis of anemia using CBC data.